R Markdown Presentation & Plotly

yhuai

24 May 2018

R Markdown

This is an R Markdown presentation. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document.

Description

Create a web page presentation using R Markdown that features a plot created with Plotly. Host your webpage on either GitHub Pages, RPubs, or NeoCities. Your webpage must contain the date that you created the document, and it must contain a plot created with Plotly. We would love to see you show off your creativity!

Slide with Plotly1

library(plotly)
datasets::mtcars
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
plot_ly(mtcars, x = mtcars$wt, y=mtcars$mpg, mode = "markers")
plot_ly(mtcars, x = mtcars$wt, y=mtcars$mpg, mode = "markers", color = as.factor(mtcars$cyl))
plot_ly(mtcars, x = mtcars$wt, y=mtcars$mpg, mode = "markers", color = mtcars$disp)
plot_ly(mtcars, x = mtcars$wt, y=mtcars$mpg, mode = "markers", color = as.factor(mtcars$cyl),size=mtcars$hp)

Slide with Plotly2

library(plotly)
set.seed(1993)
temp <- rnorm(100, mean=30, sd=5)
pressue <- rnorm(100)
dtime <- 1:100
plot_ly(x = temp, y = pressue, z = dtime,
        type = "scatter3d", mode="markers", color=temp)
data("airmiles")
airmiles
Time Series:
Start = 1937 
End = 1960 
Frequency = 1 
 [1]   412   480   683  1052  1385  1418  1634  2178  3362  5948  6109
[12]  5981  6753  8003 10566 12528 14760 16769 19819 22362 25340 25343
[23] 29269 30514
plot_ly(x=time(airmiles), y=airmiles)

Slide with Plotly3

library(plotly);library(tidyr);library(dplyr)
data("EuStockMarkets")
stocks <- as.data.frame(EuStockMarkets) %>%
    gather(index,price) %>%
    mutate(time=rep(time(EuStockMarkets),4))

plot_ly(stocks, x = stocks$time, y = stocks$price, color=stocks$index)
plot_ly(iris, x = iris$Petal.Length, type="histogram" )
plot_ly(iris, y = iris$Petal.Length, color = iris$Species, type="box" )
terrain1 <- matrix(rnorm(100*100), nrow = 100, ncol=100)
plot_ly(z=terrain1, type="heatmap")
terrain2 <- matrix(sort(rnorm(100*100)), nrow = 100, ncol=100)
plot_ly(z=terrain2, type="surface")